@inproceedings{2c8a8847ec2c40369eb0972004a81cb1,
title = "Entropy analysis of OCT signal for automatic tissue characterization",
abstract = "Optical coherence tomography (OCT) signal can provide microscopic characterization of biological tissue and assist clinical decision making in real-time. However, raw OCT data is noisy and complicated. It is challenging to extract information that is directly related to the pathological status of tissue through visual inspection on huge volume of OCT signal streaming from the high speed OCT engine. Therefore, it is critical to discover concise, comprehensible information from massive OCT data through novel strategies for signal analysis. In this study, we perform Shannon entropy analysis on OCT signal for automatic tissue characterization, which can be applied in intraoperative tumor margin delineation for surgical excision of cancer. The principle of this technique is based on the fact that normal tissue is usually more structured with higher entropy value, compared to pathological tissue such as cancer tissue. In this study, we develop high-speed software based on graphic processing units (GPU) for real-time entropy analysis of OCT signal.",
keywords = "OCT, imaging, information entropy",
author = "Yahui Wang and Yi Qiu and Farzana Zaki and Yiqing Xu and Basil Hubbi and Belfield, {Kevin D.} and Xuan Liu",
note = "Publisher Copyright: {\textcopyright} 2016 SPIE.; High-Speed Biomedical Imaging and Spectroscopy: Toward Big Data Instrumentation and Management ; Conference date: 13-02-2016 Through 14-02-2016",
year = "2016",
month = jul,
day = "18",
doi = "10.1117/12.2212587",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Keisuke Goda and Tsia, {Kevin K.}",
booktitle = "High-Speed Biomedical Imaging and Spectroscopy",
address = "United States",
}